Linking individual-tree and whole-stand models for forest growth and yield prediction

Abstract

Background: Different types of growth and yield models provide essential information for making informed decisions on how to manage forests. Whole-stand models often provide well-behaved outputs at the stand level, but lack information on stand structures. Detailed information from individual-tree models and size-class models typically suffers from accumulation of errors. The disaggregation method, in assuming that predictions from a whole-stand model are reliable, partitions these outputs to individual trees. On the other hand, the combination method seeks to improve stand-level predictions from both whole-stand and individual-tree models by combining them. Methods: Data from 100 plots randomly selected from the Southwide Seed Source Study of loblolly pine (Pinus taeda L.) were used to evaluate the unadjusted individual-tree model against the disaggregation and combination methods. Results: Compared to the whole-stand model, the combination method did not show improvements in predicting stand attributes in this study. The combination method also did not perform as well as the disaggregation method in tree-level predictions. The disaggregation method provided the best predictions of tree-and stand-level survival and growth. Conclusions: The disaggregation approach provides a link between individual-tree models and whole-stand models, and should be considered as a better alternative to the unadjusted tree model.